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pmid:20459635

Predicting environmental chemical factors associated with disease-related gene expression data.

Chirag J Patel · Atul J Butte

BMC medical genomics · 2010 doi:10.1186/1755-8794-3-17

Paper

complete

Experiment

complete

Data

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Analysis

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title
Predicting environmental chemical factors associated with disease-related gene expression data.
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10.1186/1755-8794-3-17
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20459635
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Chirag J Patel
[1]
Atul J Butte
journal
BMC medical genomics
year
2010
abstract
BACKGROUND: Many common diseases arise from an interaction between environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease. METHODS: We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test. RESULTS: We were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A. CONCLUSIONS: We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.
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We checked for enrichment of differentially expressed genes among our 1,338 chemical-gene sets with the hypergeometric test.
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sections[3] "Method to Predict Environmental Associations to Gene Expression Data" paragraph 10
quote
We checked for enrichment of differentially expressed genes among our 1,338 chemical-gene sets with the hypergeometric test.
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The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.
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sections[3] "Method to Predict Environmental Associations to Gene Expression Data" paragraph 2
quote
The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.
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The study developed a scalable statistical method that uses CTD-derived chemical–gene signatures and GEO disease gene-expression data, testing enrichment of differentially expressed genes in each chemical signature.
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sections[0] "Abstract" paragraph 2
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We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease.
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We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD).
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sections[0] "Abstract" paragraph 2
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We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.
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We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.
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sections[7] "Results" paragraph 2
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The method is centered on chemical-gene sets that are derived from single curated chemical-gene relationships in the CTD.
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sections[7] "Results" paragraph 2
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We determine whether the differentially expressed genes are associated to a chemical by assessing if the expressed genes are enriched for a chemical-gene set, or contain more genes from the chemical-gene set than expected at random using the hypergeometric test.
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6
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null
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null
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570
taxon
Homo sapiens
title
Genes regulated by vitamin D in bronchial smooth muscle cells
resolution_checked_at
2026-11-01T15:14:33Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
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The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.
structure object · 15 fields
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GEO
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GEO accession URL
access_statement_verbatim
The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.
access_statement_kind
repository_route_statement
sample_accessions array · 24 items
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GSM158658
sample_count
24
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null
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96;97
taxon
Homo sapiens
title
The prognostic role of a gene signature from tumorigenic breast-cancer cells.
resolution_checked_at
2026-11-01T15:30:21Z
resolution_check
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[9] object · 15 fields
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The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.
access_statement_kind
repository_route_statement
sample_accessions array · 504 items
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GSM152804
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[30]
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[35]
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[39]
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[40]
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[41]
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[42]
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[43]
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[44]
GSM152848
[45]
GSM152849
[46]
GSM152850
[47]
GSM152851
[48]
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[49]
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[50]
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[51]
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[52]
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[53]
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[54]
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[55]
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[56]
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[57]
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[58]
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[59]
GSM152863
[60]
GSM152864
[61]
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[62]
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[63]
GSM152867
[64]
GSM152868
[65]
GSM152869
[66]
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[67]
GSM152871
[68]
GSM152872
[69]
GSM152873
[70]
GSM152874
[71]
GSM152875
[72]
GSM152876
[73]
GSM152877
[74]
GSM152878
[75]
GSM152879
[76]
GSM152880
[77]
GSM152881
[78]
GSM152882
[79]
GSM152883
[80]
GSM152884
[81]
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[82]
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[83]
GSM152887
[84]
GSM152888
[85]
GSM152889
[86]
GSM152890
[87]
GSM152891
[88]
GSM152892
[89]
GSM152893
[90]
GSM152894
[91]
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[92]
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[93]
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[94]
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[95]
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[96]
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[97]
GSM152901
[98]
GSM152902
[99]
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[100]
GSM152904
[101]
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[102]
GSM152906
[103]
GSM152907
[104]
GSM152908
[105]
GSM152909
[106]
GSM152910
[107]
GSM152911
[108]
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[109]
GSM152913
[110]
GSM152914
[111]
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[112]
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[113]
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[114]
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[115]
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[116]
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[117]
GSM152921
[118]
GSM152922
[119]
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[120]
GSM152924
[121]
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[122]
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[123]
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[124]
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[125]
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[127]
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[128]
GSM152932
[129]
GSM152933
[130]
GSM152934
[131]
GSM152935
[132]
GSM152936
[133]
GSM152937
[134]
GSM152938
[135]
GSM152939
[136]
GSM152940
[137]
GSM152941
[138]
GSM152942
[139]
GSM152943
[140]
GSM152944
[141]
GSM152945
[142]
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[143]
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[144]
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[145]
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[146]
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[147]
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[148]
GSM152952
[149]
GSM152953
[150]
GSM152954
[151]
GSM152955
[152]
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[153]
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[154]
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[155]
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[156]
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[157]
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[158]
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[159]
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[160]
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[161]
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[162]
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[163]
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[164]
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[165]
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[166]
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[167]
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[168]
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[169]
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[170]
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[171]
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[172]
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[173]
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[174]
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[175]
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[176]
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[177]
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[178]
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[179]
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[180]
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[181]
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[182]
GSM152986
[183]
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[184]
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[185]
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[186]
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[187]
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[188]
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[189]
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[190]
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[191]
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[192]
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[193]
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[194]
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[195]
GSM152999
[196]
GSM153000
[197]
GSM153001
[198]
GSM153002
[199]
GSM153003
[200]
GSM153004
[201]
GSM153005
[202]
GSM153006
[203]
GSM153007
[204]
GSM153008
[205]
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[206]
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[207]
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[208]
GSM153012
[209]
GSM153013
[210]
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[211]
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[212]
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[213]
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[214]
GSM153018
[215]
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[216]
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[217]
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[218]
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[219]
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[220]
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[221]
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[222]
GSM153026
[223]
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[224]
GSM153028
[225]
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[226]
GSM153030
[227]
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[228]
GSM153032
[229]
GSM153033
[230]
GSM153034
[231]
GSM153035
[232]
GSM153036
[233]
GSM153037
[234]
GSM153038
[235]
GSM153039
[236]
GSM153040
[237]
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[238]
GSM153042
[239]
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[240]
GSM153044
[241]
GSM153045
[242]
GSM153046
[243]
GSM153047
[244]
GSM153048
[245]
GSM153049
[246]
GSM153050
[247]
GSM153051
[248]
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[249]
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[250]
GSM153054
[251]
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[252]
GSM153056
[253]
GSM153057
[254]
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[255]
GSM153059
[256]
GSM153060
[257]
GSM153061
[258]
GSM153062
[259]
GSM153063
[260]
GSM153064
[261]
GSM153065
[262]
GSM153066
[263]
GSM153067
[264]
GSM153068
[265]
GSM153069
[266]
GSM153070
[267]
GSM153071
[268]
GSM153072
[269]
GSM153073
[270]
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[271]
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[272]
GSM153076
[273]
GSM153077
[274]
GSM153078
[275]
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[276]
GSM153080
[277]
GSM153081
[278]
GSM153082
[279]
GSM153083
[280]
GSM153084
[281]
GSM153085
[282]
GSM153086
[283]
GSM153087
[284]
GSM153088
[285]
GSM153089
[286]
GSM153090
[287]
GSM153091
[288]
GSM153092
[289]
GSM153093
[290]
GSM153094
[291]
GSM153095
[292]
GSM153096
[293]
GSM153097
[294]
GSM153098
[295]
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[296]
GSM153100
[297]
GSM153101
[298]
GSM153102
[299]
GSM153103
[300]
GSM153104
[301]
GSM153105
[302]
GSM153106
[303]
GSM153107
[304]
GSM153108
[305]
GSM153109
[306]
GSM153110
[307]
GSM153111
[308]
GSM153112
[309]
GSM153113
[310]
GSM153114
[311]
GSM153115
[312]
GSM153116
[313]
GSM153117
[314]
GSM153118
[315]
GSM153119
[316]
GSM153120
[317]
GSM153121
[318]
GSM153122
[319]
GSM153123
[320]
GSM153124
[321]
GSM153125
[322]
GSM153126
[323]
GSM153127
[324]
GSM153128
[325]
GSM153129
[326]
GSM153130
[327]
GSM153131
[328]
GSM153132
[329]
GSM153133
[330]
GSM153134
[331]
GSM153135
[332]
GSM153136
[333]
GSM153137
[334]
GSM153138
[335]
GSM153139
[336]
GSM153140
[337]
GSM153141
[338]
GSM153142
[339]
GSM153143
[340]
GSM153144
[341]
GSM153145
[342]
GSM153146
[343]
GSM153147
[344]
GSM153148
[345]
GSM153149
[346]
GSM153150
[347]
GSM153151
[348]
GSM153152
[349]
GSM153153
[350]
GSM153154
[351]
GSM153155
[352]
GSM153156
[353]
GSM153157
[354]
GSM153158
[355]
GSM153159
[356]
GSM153160
[357]
GSM153161
[358]
GSM153162
[359]
GSM153163
[360]
GSM153164
[361]
GSM153165
[362]
GSM153166
[363]
GSM153167
[364]
GSM153168
[365]
GSM153169
[366]
GSM153170
[367]
GSM153171
[368]
GSM153172
[369]
GSM153173
[370]
GSM153174
[371]
GSM153175
[372]
GSM153176
[373]
GSM153177
[374]
GSM153178
[375]
GSM153179
[376]
GSM153180
[377]
GSM153181
[378]
GSM153182
[379]
GSM153183
[380]
GSM153184
[381]
GSM153185
[382]
GSM153186
[383]
GSM153187
[384]
GSM153188
[385]
GSM153189
[386]
GSM153190
[387]
GSM153191
[388]
GSM153192
[389]
GSM153193
[390]
GSM153194
[391]
GSM153195
[392]
GSM153196
[393]
GSM153197
[394]
GSM153198
[395]
GSM153199
[396]
GSM153200
[397]
GSM153201
[398]
GSM153202
[399]
GSM153203
[400]
GSM153204
[401]
GSM153205
[402]
GSM153206
[403]
GSM153207
[404]
GSM153208
[405]
GSM153209
[406]
GSM153210
[407]
GSM153211
[408]
GSM153212
[409]
GSM153213
[410]
GSM153214
[411]
GSM153215
[412]
GSM153216
[413]
GSM153217
[414]
GSM153218
[415]
GSM153219
[416]
GSM153220
[417]
GSM153221
[418]
GSM153222
[419]
GSM153223
[420]
GSM153224
[421]
GSM153225
[422]
GSM153226
[423]
GSM153227
[424]
GSM153228
[425]
GSM153229
[426]
GSM153230
[427]
GSM153231
[428]
GSM153232
[429]
GSM153233
[430]
GSM153234
[431]
GSM153235
[432]
GSM153236
[433]
GSM153237
[434]
GSM153238
[435]
GSM153239
[436]
GSM153240
[437]
GSM153241
[438]
GSM153242
[439]
GSM153243
[440]
GSM153244
[441]
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[442]
GSM153246
[443]
GSM153247
[444]
GSM153248
[445]
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[446]
GSM153250
[447]
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[448]
GSM153252
[449]
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[450]
GSM153254
[451]
GSM153255
[452]
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[453]
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[454]
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[455]
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[456]
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[457]
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[458]
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[459]
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[460]
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[461]
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[462]
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[463]
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[464]
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[465]
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[466]
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[467]
GSM153271
[468]
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[469]
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[470]
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[471]
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[472]
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[473]
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[474]
GSM153278
[475]
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[476]
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[477]
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[478]
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[479]
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[480]
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[481]
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[482]
GSM153286
[483]
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[484]
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[485]
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[486]
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[487]
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[488]
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[489]
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[490]
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[491]
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[492]
GSM187524
[493]
GSM187525
[494]
GSM187526
[495]
GSM187527
[496]
GSM187528
[497]
GSM187529
[498]
GSM187530
[499]
GSM187531
[500]
GSM187532
[501]
GSM187533
[502]
GSM187534
[503]
GSM187535
sample_count
504
group_sizes
null
group_sizes_basis
null
platform
93;8300;92
taxon
Homo sapiens
title
Expression Data from Normal and Prostate Tumor Tissues
resolution_checked_at
2026-11-01T15:30:21Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
[10] object · 16 fields
id
asset:table:pmid:20459635:1
role
table
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jats-table
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application/vnd.jats.table+json
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null
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sha256:0cef92c1984f87a9139213d0e37217ec44b5bb211b7350e0c428bd48af984a96
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open
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verified
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read_table
reader_status
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identifier
section[8]/table-wrap[1]#T1
version
null
note
Chemical Prediction Results from the Verification Phase.
structure object · 5 fields
pmcid
PMC2880288
section_index
8
section_title
Verification Phase
row_count
12
column_count
6
table object · 8 fields
label
Table 1
caption
Chemical Prediction Results from the Verification Phase.
rows array · 12 items
[0] array · 6 items
[0]
Actual Chemical Exposure (GEO accession)
[1]
Chemicals Predicted
[2]
Hypergeometric P-value
[3]
Rank (Percentile)
[4]
q-value
[5]
Relevant Genes Expressed
[1] array · 6 items
[0]
Vitamin D3 on H. sapiens muscle cells (GSE5145)
[1]
Calcitriol
[2]
1 × 10-23
[3]
1 (100)
[4]
0
[5]
VDR (25), CYP24A1 (14)
[2] array · 1 item
[0]
[3] array · 6 items
[0]
TCDD on M. musculus (GSE10082)
[1]
TCDD
[2]
2 × 10-15
[3]
3 (99)
[4]
0
[5]
CYP1A1 (59), CYP1B1 (15), AHRR(6), CYP1A2 (14)
[4] array · 1 item
[0]
[5] array · 6 items
[0]
Bisphenol A on H. sapiens Ishikawa cells (GSE17624)
[1]
Bisphenol A
[2]
1 × 10-6
[3]
15 (99)
[4]
0
[5]
ESR1(31), ESR2(7), S100G (6)
[6] array · 1 item
[0]
[7] array · 6 items
[0]
Zinc sulfate on H. sapiens bronchial tissue (GSE2111)
[1]
Zinc sulfate
[2]
3 × 10-3
[3]
15 (99)
[4]
0.04
[5]
SLC30A1 (3), MT1F(2), MT1G(2)
[8] array · 1 item
[0]
[9] array · 6 items
[0]
Estradiol on M. musculus thymus (GSE2889)
[1]
Estradiol
[2]
5 × 10-3
[3]
17 (99)
[4]
0.08
[5]
C3(6), LPL (4), CTSB (2)
[10] array · 1 item
[0]
[11] array · 6 items
[0]
Estradiol on H. sapiens MCF7 cell line (GSE11352)
[1]
Estradiol
[2]
5 × 10-3
[3]
19 (99)
[4]
0.08
[5]
ISG20 (2), MGP (2), SERPINA1 (2)
locator
section[8]/table-wrap[1]#T1
[11] object · 16 fields
id
asset:table:pmid:20459635:2
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:eb2fcd632393149a514deec8e5d61ccbbcb59a26f23b18149412b6e5695a7b59
bytes
1248
access
open
state
verified
reader
read_table
reader_status
available
identifier
section[9]/table-wrap[1]#T2
version
null
note
Prediction of environmental chemicals associated with prostate cancer samples (GSE6919).
structure object · 5 fields
pmcid
PMC2880288
section_index
9
section_title
Predicting Environmental Chemicals Associated with Cancer Data Sets
row_count
20
column_count
6
table object · 8 fields
label
Table 2
caption
Prediction of environmental chemicals associated with prostate cancer samples (GSE6919).
rows array · 20 items
[0] array · 6 items
[0]
Chemical Predicted
[1]
Hypergeometric P-value
[2]
Rank (percentile)
[3]
q-value
[4]
Relevant genes in set (number of references)
[5]
Citations
[1] array · 6 items
[0]
Estradiol
[1]
4 × 10-10
[2]
5 (99)
[3]
0
[4]
ESR2(37), PGR(34), MAPK1(14)
[5]
[37]
[2] array · 1 item
[0]
[3] array · 6 items
[0]
Raloxifene
[1]
1 × 10-9
[2]
6 (99)
[3]
0
[4]
ESR2(6), IGF1(5), BCL2(4)
[5]
[38]
[4] array · 1 item
[0]
[5] array · 6 items
[0]
Sodium arsenite
[1]
1 × 10-8
[2]
8 (99)
[3]
0
[4]
JUN(13), MAPK1(9), CCND1(8), FOS(6)
[5]
[30]
[6] array · 1 item
[0]
[7] array · 6 items
[0]
Doxorubicin
[1]
7 × 10-7
[2]
11 (99)
[3]
0
[4]
BCL2(23), MAPK1(14), TNF(10)
[5]
[39-42]
[8] array · 1 item
[0]
[9] array · 6 items
[0]
Cadmium
[1]
6 × 10-6
[2]
13 (99)
[3]
0
[4]
MT2A(14), MT1A(12), MT3(11), MT1(6)
[5]
[43]
[10] array · 1 item
[0]
[11] array · 6 items
[0]
Genistein
[1]
3 × 10-5
[2]
19 (99)
[3]
6 × 10-4
[4]
ESR2(22), PGR (10), MAPK1 (5)
[5]
[44-46]
[12] array · 1 item
[0]
[13] array · 6 items
[0]
Diethylstilbestrol
[1]
3 × 10-5
[2]
22 (98)
[3]
0.001
[4]
ESR2(8), FOS(8), HOXA10(4)
[5]
[47,48]
[14] array · 1 item
[0]
[15] array · 6 items
[0]
Fenretinide
[1]
3 × 10-4
[2]
40 (97)
[3]
0.004
[4]
BCL2(3), ELF3(2), LDHA(2)
[5]
[49]
[16] array · 1 item
[0]
[17] array · 6 items
[0]
Bisphenol A
[1]
6 × 10-4
[2]
47 (96)
[3]
0.01
[4]
PGR(8), ESR2(7), IL4RA(2)
[5]
[37]
[18] array · 1 item
[0]
[19] array · 6 items
[0]
Zinc
[1]
9 × 10-4
[2]
53 (96)
[3]
0.01
[4]
MT3(18), MT2A(13), MT1A(11)
[5]
[50-53]
locator
section[9]/table-wrap[1]#T2
[12] object · 16 fields
id
asset:table:pmid:20459635:3
role
table
format
jats-table
media_type
application/vnd.jats.table+json
uri
null
sha256
sha256:7f1c0719a4d0a54da4d0c09b279b415662342580dbed261499312419bba89123
bytes
1078
access
open
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verified
reader
read_table
reader_status
available
identifier
section[9]/table-wrap[2]#T3
version
null
note
Prediction of environmental chemicals associated with lung cancer samples (GSE10072).
structure object · 5 fields
pmcid
PMC2880288
section_index
9
section_title
Predicting Environmental Chemicals Associated with Cancer Data Sets
row_count
14
column_count
6
table object · 8 fields
label
Table 3
caption
Prediction of environmental chemicals associated with lung cancer samples (GSE10072).
rows array · 14 items
[0] array · 6 items
[0]
Chemical Predicted
[1]
Hypergeometric P-value
[2]
Rank (percentile)
[3]
q-value
[4]
Relevant genes in set (number of references)
[5]
Citations
[1] array · 6 items
[0]
Doxorubicin
[1]
1 × 10-6
[2]
16 (99)
[3]
4 × 10-4
[4]
CASP3(60), ABCB1(28), BAX(26), BCL2 (23)
[5]
[54]
[2] array · 1 item
[0]
[3] array · 6 items
[0]
Sodium arsenite
[1]
8 × 10-6
[2]
20 (98)
[3]
4 × 10-4
[4]
JUN(13), NQ01(6), EGR1(6)
[5]
[55-57]
[4] array · 1 item
[0]
[5] array · 6 items
[0]
Vanadium pentoxide
[1]
1 × 10-5
[2]
24 (98)
[3]
6 × 10-4
[4]
HBEGF(3), CDK7(1), CDKN1B (1), CDKN1C(1)
[5]
[58]
[6] array · 1 item
[0]
[7] array · 6 items
[0]
Dimethylnitrosamine
[1]
6 × 10-5
[2]
27 (98)
[3]
7 × 10-4
[4]
TGFB1(23), TIMP1(15), PCNA(6)
[5]
[31]
[8] array · 1 item
[0]
[9] array · 6 items
[0]
Indomethacin
[1]
2 × 10-4
[2]
34 (97)
[3]
0.002
[4]
BIRC5(3), CDKN1B(2), MMP9(2)
[5]
[59-61]
[10] array · 1 item
[0]
[11] array · 6 items
[0]
2-Acetylaminofluorene
[1]
3 × 10-4
[2]
36 (97)
[3]
0.003
[4]
ABCB1(4), ABCG2(4), KRT19(2)
[5]
[62]
[12] array · 1 item
[0]
[13] array · 6 items
[0]
Asbestos, Serpentine
[1]
4 × 10-4
[2]
39 (97)
[3]
0.004
[4]
IL6(2), MMP9(2), MMP12(2), PDGFB(2)
[5]
[63]
locator
section[9]/table-wrap[2]#T3
[13] object · 16 fields
id
asset:table:pmid:20459635:4
role
table
format
jats-table
media_type
application/vnd.jats.table+json
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null
sha256
sha256:829f06ee6e42929d00e384bd59ac54ddc7dfb929494328ab428afba0a8f768e2
bytes
1002
access
open
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verified
reader
read_table
reader_status
available
identifier
section[9]/table-wrap[3]#T4
version
null
note
Prediction of environmental chemicals associated with breast cancer samples (GSE6883).
structure object · 5 fields
pmcid
PMC2880288
section_index
9
section_title
Predicting Environmental Chemicals Associated with Cancer Data Sets
row_count
14
column_count
6
table object · 8 fields
label
Table 4
caption
Prediction of environmental chemicals associated with breast cancer samples (GSE6883).
rows array · 14 items
[0] array · 6 items
[0]
Chemical Predicted
[1]
Hypergeometric P-value
[2]
Rank (percentile)
[3]
q-value
[4]
Relevant genes in set (number of references)
[5]
Citations
[1] array · 6 items
[0]
Progesterone
[1]
2 × 10-4
[2]
6 (99)
[3]
0.01
[4]
IL6(3), STC1(3), CEBPD(2)
[5]
[64,65]
[2] array · 1 item
[0]
[3] array · 6 items
[0]
Genistein
[1]
6 × 10-4
[2]
10 (99)
[3]
0.03
[4]
CEBPD(1), APLP2(1), MLF1(1)
[5]
[66-68]
[4] array · 1 item
[0]
[5] array · 6 items
[0]
Estradiol
[1]
7 × 10-4
[2]
11 (99)
[3]
0.03
[4]
LPL(4), IL6(3), CEBPD(2)
[5]
[69-73]
[6] array · 1 item
[0]
[7] array · 6 items
[0]
Indomethacin
[1]
3 × 10-3
[2]
17 (99)
[3]
0.05
[4]
CCDC50(1), BIRC3(1), DNAJB(1)
[5]
[74]
[8] array · 1 item
[0]
[9] array · 6 items
[0]
Diethylstilbestrol
[1]
3 × 10-3
[2]
18 (99)
[3]
0.05
[4]
IL6(1), MARCKS(1), MXD1(1), MMP7(1)
[5]
[75,76]
[10] array · 1 item
[0]
[11] array · 6 items
[0]
Cyclophosphamide
[1]
4 × 10-4
[2]
19 (99)
[3]
0.06
[4]
IL6(3), MARCKS(1), PSMA5(1)
[5]
[77-79]
[12] array · 1 item
[0]
[13] array · 6 items
[0]
Bisphenol A
[1]
6 × 10-3
[2]
21 (99)
[3]
0.08
[4]
CEBPD(1), MLF1(1), DTL(1)
[5]
[80]
locator
section[9]/table-wrap[3]#T4
gaps array · 18 items
[0]
asset:data:geo:GSE10072 not downloaded
[1]
asset:data:geo:GSE10072 licence not read
[2]
asset:data:geo:GSE10082 not downloaded
[3]
asset:data:geo:GSE10082 licence not read
[4]
asset:data:geo:GSE11352 not downloaded
[5]
asset:data:geo:GSE11352 licence not read
[6]
asset:data:geo:GSE17624 not downloaded
[7]
asset:data:geo:GSE17624 licence not read
[8]
asset:data:geo:GSE2111 not downloaded
[9]
asset:data:geo:GSE2111 licence not read
[10]
asset:data:geo:GSE2889 not downloaded
[11]
asset:data:geo:GSE2889 licence not read
[12]
asset:data:geo:GSE5145 not downloaded
[13]
asset:data:geo:GSE5145 licence not read
[14]
asset:data:geo:GSE6883 not downloaded
[15]
asset:data:geo:GSE6883 licence not read
[16]
asset:data:geo:GSE6919 not downloaded
[17]
asset:data:geo:GSE6919 licence not read
analysis object · 4 fields
state
partial
methods array · 1 item
[0] object · 10 fields
id
method-1
family
null
specific
null
label
hypergeometric test
dsl_operations array · 0 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 0 items
evidence object · 3 fields
score
1
band
verbatim
quote
We checked for enrichment of differentially expressed genes among our 1,338 chemical-gene sets with the hypergeometric test.
targets array · 0 items
gaps array · 1 item
[0]
method.reader_named_unmapped
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
blocked
tier
R4
blocking_axis
method
blocked_on
method:not_determinable
axes object · 3 fields
method object · 5 fields
tier
R4
expressible
false
required_operations array · 0 items
missing_operations array · 0 items
basis
The reader quoted the method the paper names (hypergeometric test), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 9 items
[0]
GSE5145
[1]
GSE10082
[2]
GSE17624
[3]
GSE2111
[4]
GSE2889
[5]
GSE11352
[6]
GSE6919
[7]
GSE10072
[8]
GSE6883
resolved_accessions array · 9 items
[0]
GSE5145
[1]
GSE10082
[2]
GSE17624
[3]
GSE2111
[4]
GSE2889
[5]
GSE11352
[6]
GSE6919
[7]
GSE10072
[8]
GSE6883
statements array · 1 item
[0]
The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.
basis
The paper names GSE5145, GSE10082, GSE17624, GSE2111, GSE2889, GSE11352, GSE6919, GSE10072, GSE6883 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 0 items
assets array · 6 items
[0]
source:pmid:20459635
[1]
data:geo:GSE10072
[2]
asset:table:pmid:20459635:1
[3]
asset:table:pmid:20459635:2
[4]
asset:table:pmid:20459635:3
[5]
asset:table:pmid:20459635:4
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE10072
gaps array · 1 item
[0]
method:not_determinable
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-10-31T18:20:50Z
independence
same_provider
evidence_check object · 2 fields
method
verbatim
availability
null
calls
8
usage object · 3 fields
prompt_tokens
76362
completion_tokens
8117
cached_tokens
57460
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:20459635
type
paper
label
pmid:20459635
[1] object · 3 fields
id
source:pmid:20459635
type
manuscript
label
PMID 20459635 full text
[2] object · 3 fields
id
data:geo:GSE10072
type
dataset
label
GEO GSE10072
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
classification
used array · 3 items
[0] object · 3 fields
id
pmid:20459635
type
paper
label
pmid:20459635
[1] object · 3 fields
id
source:pmid:20459635
type
manuscript
label
PMID 20459635 full text
[2] object · 3 fields
id
data:geo:GSE10072
type
dataset
label
GEO GSE10072
agent
agent:corpus-assay-engine
[1] object · 4 fields
id
activity-read-20459635
type
extraction
used array · 1 item
[0]
source:pmid:20459635
agent
agent:minimax-reader
[2] object · 4 fields
id
activity-resolve-GSE10072
type
accession_resolution
used array · 1 item
[0]
data:geo:GSE10072
agent
agent:corpus-assay-engine
agents array · 2 items
[0] object · 3 fields
id
agent:corpus-assay-engine
type
software
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Corpus assay engine
[1] object · 3 fields
id
agent:minimax-reader
type
software
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MiniMax literature reader
source_run_id
reading-sci-2026-11-01
dsl_catalogue_fingerprint
343fb30756ba6ec3
capability_table_fingerprint
2cb3e42f047d2ecf
snapshot_id
ba34b49490d9bfaf
lineage object · 4 fields
parent_id
pmid:20459635
parent_hash
sha256:3e777b714b9630fed757408abf156081ebfb7a0829a34dd3c874f59debb30a7a
fork_of
null
gaps array · 23 items
[0]
asset:data:geo:GSE10072 licence not read
[1]
asset:data:geo:GSE10072 not downloaded
[2]
asset:data:geo:GSE10082 licence not read
[3]
asset:data:geo:GSE10082 not downloaded
[4]
asset:data:geo:GSE11352 licence not read
[5]
asset:data:geo:GSE11352 not downloaded
[6]
asset:data:geo:GSE17624 licence not read
[7]
asset:data:geo:GSE17624 not downloaded
[8]
asset:data:geo:GSE2111 licence not read
[9]
asset:data:geo:GSE2111 not downloaded
[10]
asset:data:geo:GSE2889 licence not read
[11]
asset:data:geo:GSE2889 not downloaded
[12]
asset:data:geo:GSE5145 licence not read
[13]
asset:data:geo:GSE5145 not downloaded
[14]
asset:data:geo:GSE6883 licence not read
[15]
asset:data:geo:GSE6883 not downloaded
[16]
asset:data:geo:GSE6919 licence not read
[17]
asset:data:geo:GSE6919 not downloaded
[18]
execution.not_run
[19]
method.reader_named_unmapped
[20]
review.human_critique
[21]
review.independent_reader
[22]
review.machine_critique
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[0] object · 11 fields
role
data_generation
definition_id
microarray
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.99
evidence array · 4 items
[0]
y available disease-specific gene expression microarray data and curated chemical-gene interaction d
[1]
select differentially expressed genes from a microarray experiment [14]. The FDR for SAM for all of
[2]
rder to reduce false associations. We mapped microarray annotations to other corresponding represent
[3]
ing 100 random resamplings of genes from the microarray experiment and testing each of these random
order_basis
paper_text_order:620
id
assay:1
[1] object · 11 fields
role
data_generation
definition_id
cell_culture
definition_version
99e1195c7d6f
required_operations array · 0 items
support_state
partial
source_hashes object · 1 field
assay_lexicon
99e1195c7d6f
confidence
0.77
evidence array · 1 item
[0]
ally, set 6 was GSE11352 in which H. sapiens MCF-7 cell line was exposed to estradiol at 3 diff
order_basis
paper_text_order:8793
id
assay:2
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:20459635", "kind": "science-record", "revision": 8, "content_hash": "sha256:3474e28ccfedf028195524c445b24b0633381bd269800a3c39c0dcb5c1e3063d", "created_at": "2026-11-01T15:14:34Z", "updated_at": "2026-11-01T21:19:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Predicting environmental chemical factors associated with disease-related gene expression data.", "doi": "10.1186/1755-8794-3-17", "pmid": "20459635", "authors": ["Chirag J Patel", "Atul J Butte"], "journal": "BMC medical genomics", "year": 2010, "abstract": "BACKGROUND: Many common diseases arise from an interaction between environmental and genetic factors. Our knowledge regarding environment and gene interactions is growing, but frameworks to build an association between gene-environment interactions and disease using preexisting, publicly available data has been lacking. Integrating freely-available environment-gene interaction and disease phenotype data would allow hypothesis generation for potential environmental associations to disease.\nMETHODS: We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease. We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD). We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test.\nRESULTS: We were able to verify our analytic method by accurately identifying chemicals applied to samples and cell lines. Furthermore, we were able to predict known and novel environmental associations with prostate, lung, and breast cancers, such as estradiol and bisphenol A.\nCONCLUSIONS: We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature.", "claims": [{"id": "claim-method", "statement": "We checked for enrichment of differentially expressed genes among our 1,338 chemical-gene sets with the hypergeometric test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:20459635", "locator": "sections[3] \"Method to Predict Environmental Associations to Gene Expression Data\" paragraph 10", "quote": "We checked for enrichment of differentially expressed genes among our 1,338 chemical-gene sets with the hypergeometric test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:20459635", "locator": "sections[3] \"Method to Predict Environmental Associations to Gene Expression Data\" paragraph 2", "quote": "The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study developed a scalable statistical method that uses CTD-derived chemical–gene signatures and GEO disease gene-expression data, testing enrichment of differentially expressed genes in each chemical signature.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:20459635", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We integrated publicly available disease-specific gene expression microarray data and curated chemical-gene interaction data to systematically predict environmental chemicals associated with disease."}, {"asset": "source:pmid:20459635", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We derived chemical-gene signatures for 1,338 chemical/environmental chemicals from the Comparative Toxicogenomics Database (CTD)."}, {"asset": "source:pmid:20459635", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We associated these chemical-gene signatures with differentially expressed genes from datasets found in the Gene Expression Omnibus (GEO) through an enrichment test."}, {"asset": "source:pmid:20459635", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We have developed a scalable and statistical method to identify possible environmental associations with disease using publicly available data and have validated some of the associations in the literature."}, {"asset": "source:pmid:20459635", "locator": "sections[7] \"Results\" paragraph 2", "quote": "The method is centered on chemical-gene sets that are derived from single curated chemical-gene relationships in the CTD."}, {"asset": "source:pmid:20459635", "locator": "sections[7] \"Results\" paragraph 2", "quote": "We determine whether the differentially expressed genes are associated to a chemical by assessing if the expressed genes are enriched for a chemical-gene set, or contain more genes from the chemical-gene set than expected at random using the hypergeometric test."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study developed a scalable statistical method that uses CTD-derived chemical–gene signatures and GEO disease gene-expression data, testing enrichment of differentially expressed genes in each chemical signature.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:20459635"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM63205", "GSM63206", "GSM63207", "GSM63208", "GSM63210", "GSM63211", "GSM63212", "GSM63213", "GSM63214", "GSM63215", "GSM63216", "GSM63217", "GSM63218", "GSM63220"], "sample_count": 14, "group_sizes": null, "group_sizes_basis": null, "platform": "339", "taxon": "Mus musculus", "title": "Gene Expression Profiling of 17 beta-Estradiol and Genistein Effects on Mouse Thymus", "resolution_checked_at": "2026-11-01T15:30:21Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE5145", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5145", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE5145", "version": null, "note": "The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. 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We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM158635", "GSM158636", "GSM158637", "GSM158638", "GSM158639", "GSM158640", "GSM158641", "GSM158642", "GSM158643", "GSM158644", "GSM158645", "GSM158646", "GSM158647", "GSM158648", "GSM158649", "GSM158650", "GSM158651", "GSM158652", "GSM158653", "GSM158654", "GSM158655", "GSM158656", "GSM158657", "GSM158658"], "sample_count": 24, "group_sizes": null, "group_sizes_basis": null, "platform": "96;97", "taxon": "Homo sapiens", "title": "The prognostic role of a gene signature from tumorigenic breast-cancer cells.", "resolution_checked_at": "2026-11-01T15:30:21Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE6919", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6919", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE6919", "version": null, "note": "The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. 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We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM152804", "GSM152805", "GSM152806", "GSM152807", "GSM152808", "GSM152809", "GSM152810", "GSM152811", "GSM152812", "GSM152813", "GSM152814", "GSM152815", "GSM152816", "GSM152817", "GSM152818", "GSM152819", "GSM152820", "GSM152821", "GSM152822", "GSM152823", "GSM152824", "GSM152825", "GSM152826", "GSM152827", "GSM152828", "GSM152829", "GSM152830", "GSM152831", "GSM152832", "GSM152833", "GSM152834", "GSM152835", "GSM152836", "GSM152837", "GSM152838", "GSM152839", "GSM152840", "GSM152841", "GSM152842", "GSM152843", "GSM152844", "GSM152845", "GSM152846", "GSM152847", "GSM152848", "GSM152849", "GSM152850", "GSM152851", "GSM152852", "GSM152853", "GSM152854", "GSM152855", "GSM152856", "GSM152857", "GSM152858", "GSM152859", "GSM152860", "GSM152861", "GSM152862", "GSM152863", 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"GSM153249", "GSM153250", "GSM153251", "GSM153252", "GSM153253", "GSM153254", "GSM153255", "GSM153256", "GSM153257", "GSM153258", "GSM153259", "GSM153260", "GSM153261", "GSM153262", "GSM153263", "GSM153264", "GSM153265", "GSM153266", "GSM153267", "GSM153268", "GSM153269", "GSM153270", "GSM153271", "GSM153272", "GSM153273", "GSM153274", "GSM153275", "GSM153276", "GSM153277", "GSM153278", "GSM153279", "GSM153280", "GSM153281", "GSM153282", "GSM153283", "GSM153284", "GSM153285", "GSM153286", "GSM153287", "GSM153288", "GSM153289", "GSM153290", "GSM153291", "GSM153292", "GSM153293", "GSM153294", "GSM153295", "GSM187524", "GSM187525", "GSM187526", "GSM187527", "GSM187528", "GSM187529", "GSM187530", "GSM187531", "GSM187532", "GSM187533", "GSM187534", "GSM187535"], "sample_count": 504, "group_sizes": null, "group_sizes_basis": null, "platform": "93;8300;92", "taxon": "Homo sapiens", "title": "Expression Data from Normal and Prostate Tumor Tissues", "resolution_checked_at": "2026-11-01T15:30:21Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "asset:table:pmid:20459635:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:0cef92c1984f87a9139213d0e37217ec44b5bb211b7350e0c428bd48af984a96", "bytes": 1117, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[8]/table-wrap[1]#T1", "version": null, "note": "Chemical Prediction Results from the Verification Phase.", "structure": {"pmcid": "PMC2880288", "section_index": 8, "section_title": "Verification Phase", "row_count": 12, "column_count": 6}, "table": {"label": "Table 1", "caption": "Chemical Prediction Results from the Verification Phase.", "rows": [["Actual Chemical Exposure (GEO accession)", "Chemicals Predicted", "Hypergeometric P-value", "Rank (Percentile)", "q-value", "Relevant Genes Expressed"], ["Vitamin D3 on H. sapiens muscle cells (GSE5145)", "Calcitriol", "1 × 10-23", "1 (100)", "0", "VDR (25), CYP24A1 (14)"], [""], ["TCDD on M. musculus (GSE10082)", "TCDD", "2 × 10-15", "3 (99)", "0", "CYP1A1 (59), CYP1B1 (15), AHRR(6), CYP1A2 (14)"], [""], ["Bisphenol A on H. sapiens Ishikawa cells (GSE17624)", "Bisphenol A", "1 × 10-6", "15 (99)", "0", "ESR1(31), ESR2(7), S100G (6)"], [""], ["Zinc sulfate on H. sapiens bronchial tissue (GSE2111)", "Zinc sulfate", "3 × 10-3", "15 (99)", "0.04", "SLC30A1 (3), MT1F(2), MT1G(2)"], [""], ["Estradiol on M. musculus thymus (GSE2889)", "Estradiol", "5 × 10-3", "17 (99)", "0.08", "C3(6), LPL (4), CTSB (2)"], [""], ["Estradiol on H. sapiens MCF7 cell line (GSE11352)", "Estradiol", "5 × 10-3", "19 (99)", "0.08", "ISG20 (2), MGP (2), SERPINA1 (2)"]], "row_count": 12, "column_count": 6, "numeric_cells": 30, "header_rows": 1, "locator": "section[8]/table-wrap[1]#T1"}}, {"id": "asset:table:pmid:20459635:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:eb2fcd632393149a514deec8e5d61ccbbcb59a26f23b18149412b6e5695a7b59", "bytes": 1248, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[9]/table-wrap[1]#T2", "version": null, "note": "Prediction of environmental chemicals associated with prostate cancer samples (GSE6919).", "structure": {"pmcid": "PMC2880288", "section_index": 9, "section_title": "Predicting Environmental Chemicals Associated with Cancer Data Sets", "row_count": 20, "column_count": 6}, "table": {"label": "Table 2", "caption": "Prediction of environmental chemicals associated with prostate cancer samples (GSE6919).", "rows": [["Chemical Predicted", "Hypergeometric P-value", "Rank (percentile)", "q-value", "Relevant genes in set (number of references)", "Citations"], ["Estradiol", "4 × 10-10", "5 (99)", "0", "ESR2(37), PGR(34), MAPK1(14)", "[37]"], [""], ["Raloxifene", "1 × 10-9", "6 (99)", "0", "ESR2(6), IGF1(5), BCL2(4)", "[38]"], [""], ["Sodium arsenite", "1 × 10-8", "8 (99)", "0", "JUN(13), MAPK1(9), CCND1(8), FOS(6)", "[30]"], [""], ["Doxorubicin", "7 × 10-7", "11 (99)", "0", "BCL2(23), MAPK1(14), TNF(10)", "[39-42]"], [""], ["Cadmium", "6 × 10-6", "13 (99)", "0", "MT2A(14), MT1A(12), MT3(11), MT1(6)", "[43]"], [""], ["Genistein", "3 × 10-5", "19 (99)", "6 × 10-4", "ESR2(22), PGR (10), MAPK1 (5)", "[44-46]"], [""], ["Diethylstilbestrol", "3 × 10-5", "22 (98)", "0.001", "ESR2(8), FOS(8), HOXA10(4)", "[47,48]"], [""], ["Fenretinide", "3 × 10-4", "40 (97)", "0.004", "BCL2(3), ELF3(2), LDHA(2)", "[49]"], [""], ["Bisphenol A", "6 × 10-4", "47 (96)", "0.01", "PGR(8), ESR2(7), IL4RA(2)", "[37]"], [""], ["Zinc", "9 × 10-4", "53 (96)", "0.01", "MT3(18), MT2A(13), MT1A(11)", "[50-53]"]], "row_count": 20, "column_count": 6, "numeric_cells": 50, "header_rows": 1, "locator": "section[9]/table-wrap[1]#T2"}}, {"id": "asset:table:pmid:20459635:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:7f1c0719a4d0a54da4d0c09b279b415662342580dbed261499312419bba89123", "bytes": 1078, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[9]/table-wrap[2]#T3", "version": null, "note": "Prediction of environmental chemicals associated with lung cancer samples (GSE10072).", "structure": {"pmcid": "PMC2880288", "section_index": 9, "section_title": "Predicting Environmental Chemicals Associated with Cancer Data Sets", "row_count": 14, "column_count": 6}, "table": {"label": "Table 3", "caption": "Prediction of environmental chemicals associated with lung cancer samples (GSE10072).", "rows": [["Chemical Predicted", "Hypergeometric P-value", "Rank (percentile)", "q-value", "Relevant genes in set (number of references)", "Citations"], ["Doxorubicin", "1 × 10-6", "16 (99)", "4 × 10-4", "CASP3(60), ABCB1(28), BAX(26), BCL2 (23)", "[54]"], [""], ["Sodium arsenite", "8 × 10-6", "20 (98)", "4 × 10-4", "JUN(13), NQ01(6), EGR1(6)", "[55-57]"], [""], ["Vanadium pentoxide", "1 × 10-5", "24 (98)", "6 × 10-4", "HBEGF(3), CDK7(1), CDKN1B (1), CDKN1C(1)", "[58]"], [""], ["Dimethylnitrosamine", "6 × 10-5", "27 (98)", "7 × 10-4", "TGFB1(23), TIMP1(15), PCNA(6)", "[31]"], [""], ["Indomethacin", "2 × 10-4", "34 (97)", "0.002", "BIRC5(3), CDKN1B(2), MMP9(2)", "[59-61]"], [""], ["2-Acetylaminofluorene", "3 × 10-4", "36 (97)", "0.003", "ABCB1(4), ABCG2(4), KRT19(2)", "[62]"], [""], ["Asbestos, Serpentine", "4 × 10-4", "39 (97)", "0.004", "IL6(2), MMP9(2), MMP12(2), PDGFB(2)", "[63]"]], "row_count": 14, "column_count": 6, "numeric_cells": 36, "header_rows": 1, "locator": "section[9]/table-wrap[2]#T3"}}, {"id": "asset:table:pmid:20459635:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:829f06ee6e42929d00e384bd59ac54ddc7dfb929494328ab428afba0a8f768e2", "bytes": 1002, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[9]/table-wrap[3]#T4", "version": null, "note": "Prediction of environmental chemicals associated with breast cancer samples (GSE6883).", "structure": {"pmcid": "PMC2880288", "section_index": 9, "section_title": "Predicting Environmental Chemicals Associated with Cancer Data Sets", "row_count": 14, "column_count": 6}, "table": {"label": "Table 4", "caption": "Prediction of environmental chemicals associated with breast cancer samples (GSE6883).", "rows": [["Chemical Predicted", "Hypergeometric P-value", "Rank (percentile)", "q-value", "Relevant genes in set (number of references)", "Citations"], ["Progesterone", "2 × 10-4", "6 (99)", "0.01", "IL6(3), STC1(3), CEBPD(2)", "[64,65]"], [""], ["Genistein", "6 × 10-4", "10 (99)", "0.03", "CEBPD(1), APLP2(1), MLF1(1)", "[66-68]"], [""], ["Estradiol", "7 × 10-4", "11 (99)", "0.03", "LPL(4), IL6(3), CEBPD(2)", "[69-73]"], [""], ["Indomethacin", "3 × 10-3", "17 (99)", "0.05", "CCDC50(1), BIRC3(1), DNAJB(1)", "[74]"], [""], ["Diethylstilbestrol", "3 × 10-3", "18 (99)", "0.05", "IL6(1), MARCKS(1), MXD1(1), MMP7(1)", "[75,76]"], [""], ["Cyclophosphamide", "4 × 10-4", "19 (99)", "0.06", "IL6(3), MARCKS(1), PSMA5(1)", "[77-79]"], [""], ["Bisphenol A", "6 × 10-3", "21 (99)", "0.08", "CEBPD(1), MLF1(1), DTL(1)", "[80]"]], "row_count": 14, "column_count": 6, "numeric_cells": 35, "header_rows": 1, "locator": "section[9]/table-wrap[3]#T4"}}], "gaps": ["asset:data:geo:GSE10072 not downloaded", "asset:data:geo:GSE10072 licence not read", "asset:data:geo:GSE10082 not downloaded", "asset:data:geo:GSE10082 licence not read", "asset:data:geo:GSE11352 not downloaded", "asset:data:geo:GSE11352 licence not read", "asset:data:geo:GSE17624 not downloaded", "asset:data:geo:GSE17624 licence not read", "asset:data:geo:GSE2111 not downloaded", "asset:data:geo:GSE2111 licence not read", "asset:data:geo:GSE2889 not downloaded", "asset:data:geo:GSE2889 licence not read", "asset:data:geo:GSE5145 not downloaded", "asset:data:geo:GSE5145 licence not read", "asset:data:geo:GSE6883 not downloaded", "asset:data:geo:GSE6883 licence not read", "asset:data:geo:GSE6919 not downloaded", "asset:data:geo:GSE6919 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "hypergeometric test", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We checked for enrichment of differentially expressed genes among our 1,338 chemical-gene sets with the hypergeometric test."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE5145", "GSE10082", "GSE17624", "GSE2111", "GSE2889", "GSE11352", "GSE6919", "GSE10072", "GSE6883"], "resolved_accessions": ["GSE5145", "GSE10082", "GSE17624", "GSE2111", "GSE2889", "GSE11352", "GSE6919", "GSE10072", "GSE6883"], "statements": ["The Comparative Toxicogenomics Database (CTD) includes manually-curated, cross-species relations between chemicals and genes, proteins, and mRNA transcripts [13]. We downloaded the knowledge-base spanning 4,078 chemicals and 15 461 genes and 85 937 relationships between them in January 2009."], "basis": "The paper names GSE5145, GSE10082, GSE17624, GSE2111, GSE2889, GSE11352, GSE6919, GSE10072, GSE6883 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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